# Copyright 2024 the LlamaFactory team.
# Copyright (c) 2024 Huawei Technologies Co., Ltd.
#
# This code is inspired by the LLaMA-Factory.
# https://github.com/hiyouga/LLaMA-Factory/blob/main/src/llamafactory/chat/chat_model.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from abc import ABC, abstractmethod
from typing import Dict, Any, AsyncGenerator
from transformers import PreTrainedModel, PreTrainedTokenizer

from openmind.flow.datasets.template import Template


class BaseEngine(ABC):
    r"""
    Base class for inference engine of chat models.

    Must implements async methods: chat(), stream_chat() and get_scores().
    """

    model: "PreTrainedModel"
    tokenizer: "PreTrainedTokenizer"
    can_generate: bool
    template: "Template"
    generating_args: Dict[str, Any]

    @abstractmethod
    def __init__(self, datasets_args, model_args, finetune_args, training_args, generation_args) -> None:
        r"""
        Initializes an inference engine.
        """
        ...

    @abstractmethod
    async def stream_chat(
        self,
        model,
        tokenizer,
        template,
        datasets_args,
        generation_args,
        messages_context,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]:
        r"""
        Gets the response token-by-token of the chat model.
        """
        ...
